This paper analyzes privacy threats in federated matrix factorization.
problem Privacy threats in federated matrix factorization models.
method Categorizes federated matrix factorization into three types and analyzes privacy threats.
result This is the first study of privacy threats in federated matrix factorization.
Federated multi-view matrix factorization learns from multiple data sources without centralizing user data.
problem Cold-start federated recommendations and multi-view data structure.
method Federated learning framework extended to multi-view matrix factorization.
result Federated multi-view matrix factorization outperforms simpler methods in cold-start federated recommendations.
Federated learning approach for binary matrix factorization.
problem Efficiently factorizing binary data distributed across stakeholders while maintaining privacy.
method Proximal optimization for federated learning of relaxed binary matrix factorization.
result Federated algorithm outperforms state-of-the-art methods in quality and efficacy.
SharedMF uses secret sharing to protect privacy in distributed recommendation systems.
problem Privacy issues in multi-source data for recommendation systems.
method Federated learning and secret sharing technology.
result SharedMF achieves faster execution speed and better data adaptability compared to homomorphic encryption methods.
Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks. Although many FL algorithms have been proposed, few of them have considered the matrix factorization (MF) model, which is known to have a vast number of signal processing …
The paper analyzes privacy leakage in federated learning using linear algebra and optimization theory.
problem Privacy leakage in federated learning despite its promise for data privacy.
method Theoretical analysis from linear algebra and optimization theory perspectives.
result Derives sufficient conditions to prevent data reconstruction attacks and establishes an upper bound on privacy leakage.
Paper develops Gaussian approximations and bootstrap for federated LSA with trade-off bounds.
problem Analyzing convergence rates and trade-offs in federated linear stochastic approximation.
method Established Berry-Esseen-type bounds for federated LSA, developed multiplier bootstrap for inference.
result First federated Gaussian approximations with explicit trade-off terms and non-asymptotic validity guarantees.
Paper accelerates and secures distributed NMF.
problem Efficiently processing large NMF matrices and maintaining data privacy.
method Proposes DSANLS framework with matrix sketching for acceleration and secure adaptation.
result DSANLS framework and secure adaptations for distributed NMF.
FedSplit improves federated learning for heterogeneous data.
problem Data heterogeneity in federated learning degrades convergence and performance.
method FedSplit splits data into shared and personalized groups, optimizing a novel objective function.
result FedSplit converges faster and performs better than standard federated learning.
New algorithm reduces sample and communication complexities in federated Q-learning.
problem Optimal Q-function learning in federated Q-learning with limited communication.
method Introduced Fed-DVR-Q algorithm for order-optimal sample and communication complexities.
result Complete characterization of sample-communication complexity trade-off.
Paper explores subdifferential chain rules for matrix factorization and related machine learning models.
problem Clarke subdifferential chain rules for matrix factorization and factorization machines.
method Analyzes conditions for subdifferential chain rules to hold, especially for overparameterized models.
result Subdifferential chain rules hold for matrix factorization and factorization machines under certain conditions.
Machine learning methods allow us to make recommendations to users in applications across fields including entertainment, dating, and commerce, by exploiting similarities in users' interaction patterns. However, in domains that demand protection of personally sensitive data, such as medicine or banking, how can we lear…
Paper optimizes federated PCA for covariance estimation under privacy constraints.
problem Privacy-preserving covariance estimation in federated learning.
method Federated PCA, matrix version of van Trees' inequality, three-layer spectral decomposition.
result Optimal rates of convergence for central server's estimation, robust to inconsistent local estimators.
WAFFLe anonymizes federated learning weights to protect data privacy and fairness.
problem Federated learning exposes local models to attacks and underfits heterogeneous clients.
method Combines Indian Buffet Process with shared weight factors.
result Significant improvement in local test performance and fairness.
Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning …
FLANDERS detects and blocks extreme model poisoning in federated learning.
problem Resilience against large-scale model poisoning attacks in federated learning.
method FLANDERS treats client updates as matrix-valued time series and identifies outliers using autoregressive forecasting.
result FLANDERS significantly improves robustness in federated learning across various attacks.
Proposes SROF for row-wise fusion in federated learning for multivariate responses.
problem Heterogeneous client models with shared variable-level structure.
method Sparse Row-wise Fusion (SROF) regularizer and RowFed algorithm.
result Empirically shows consistent error reduction and stronger variable-level cluster recovery.
Reverse annealing boosts quantum matrix factorization performance.
problem Improving quantum matrix factorization performance.
method Combining forward and reverse annealing for nonnegative/binary matrix factorization.
result Combination of forward and reverse annealing significantly improves performance.
Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of variou…
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in da…
Framework for joint inference of network topology and interaction types in heterogeneous systems.
problem Joint inference of network topology, multi-type interaction kernels, and latent type assignments in heterogeneous interacting particle systems.
method Three-stage approach: shared structure recovery, discrete interaction type identification, and matrix factorization.
result The method yields accurate reconstruction of underlying dynamics and is robust to noise.
Proposes D-CDLF for multi-view data decomposition.
problem Uncorrelatedness between common and distinctive latent factors.
method Decomposes data into common, distinctive, and noise components.
result Effective uncorrelatedness between distinctive latent factors from different views.
FTTQ optimizes quantized networks in federated learning, reducing communication costs.
problem Redundant parameters in full-precision models lead to excessive communication costs in federated learning.
method FTTQ algorithm that optimizes quantized networks on clients through self-learning quantization factors.
result FTTQ reduces communication costs and can achieve slightly better performance on non-IID data.
The paper proposes methods for predicting missing values in mixed data matrices.
problem Matrix completion for mixed data types (continuous, binary, ordinal).
method Generalized latent factor models for low-rank matrix estimation with entrywise consistency.
result Tight probabilistic error bounds for the proposed estimators.
Advances neural tri-factorization for clustering and discordance analysis of multi-typed data.
problem Challenges in analyzing heterogeneous, multimodal relational data.
method Deep collective matrix tri-factorization for spectral clustering and cluster association learning.
result Demonstrates efficacy over previous non-neural approaches in clustering and discordance analysis.
Existing nonnegative matrix factorization methods focus on learning global structure of the data to construct basis and coefficient matrices, which ignores the local structure that commonly exists among data. In this paper, we propose a new type of nonnegative matrix factorization method, which learns local similarity …
We present a federated, asynchronous, and (ε,δ)-differentially private algorithm for PCA in the memory-limited setting. Our algorithm incrementally computes local model updates using a streaming procedure and adaptively estimates its r leading principal components when only O(dr) memory is av…
Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospi…
Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing s…
Deep tensor factorization benefits from implicit regularization with polynomial growth.
problem Tensor factorization's implicit regularization effect in deep networks is not well understood.
method Investigated the implicit regularization in deep tensor factorization, showing polynomial growth.
result Implicit regularization in deep tensor factorization grows polynomially with depth, improving estimation accuracy and convergence.
Efficient federated algorithm for calculating transportation barycenter.
problem Efficiently calculating the free-support transportation barycenter in a federated setting.
method Single-loop dual decomposition algorithm that uses only aggregated information.
result Significantly scalable and low-complexity algorithm for federated computation.
We study the problem of large-scale network embedding, which aims to learn latent representations for network mining applications. Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2)the explicit factorization o…
Proposes a method to cluster multi-aspect data using manifold learning with NMF.
problem Clustering multi-aspect data with diverse features and views.
method Includes inter-manifold learning in NMF framework to handle different data types.
result The method improves clustering accuracy and efficiency on various datasets.
The paper develops concentration inequalities for structured random data, extending beyond independent terms.
problem Developing concentration inequalities for structured weighted sums of random data, including tensors and matrix-valued data.
method The paper develops Hoeffding and Bernstein bounds for structured weighted sums under exchangeability, extending beyond the classical framework of independent terms.
result The paper develops a sharper concentration bound for combinatorial sums of matrix arrays.
New method efficiently learns positive-definite curvature for neural nets.
problem Efficiently learn positive-definite curvature for neural net training.
method Spectral-factorized positive-definite curvature learning approach.
result Efficiently applies arbitrary matrix roots and generic curvature learning.
We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in signal processing and machine learning. The new framework is a hybrid between alternating optimization (AO) and the alternating direction method of multipliers (ADMM): each matrix factor is updated in tur…
Paper tackles federated learning with privacy, enhancing target data analysis.
problem Heterogeneity and privacy of distributed data in federated learning.
method Formulates federated differential privacy, studies statistical problems under privacy constraints.
result Federated differential privacy offers a balance between privacy and knowledge transfer.
Paper studies user-level differential privacy in federated linear contextual bandits.
problem Federated learning with user-level differential privacy constraints.
method Unified federated bandits framework, CDP and LDP definitions, ROBIN algorithm.
result Near-optimal learning under user-level CDP with privacy budget and number of clients.
New algorithm reduces dimensionality in federated learning.
problem Estimating central dimension reduction subspace and variable selection in federated learning.
method Federated sparse sliced inverse regression, convex optimization, linearized alternating direction method of multipliers.
result Upper bound of statistical error rate established under heterogeneous setting.
A new method combines multiple cancer datasets to improve analysis.
problem Combining multiple cancer datasets for comprehensive analysis.
method Multiple Augmented Reduced Rank Regression (maRRR) method.
result Improved power and insights from combining multiple cancer datasets.
Paper studies federated nonparametric testing with privacy constraints, achieving optimal rates and adaptive testing.
problem Federated nonparametric goodness-of-fit testing under distributed differential privacy constraints.
method Establishes matching lower and upper bounds on minimax separation rate, constructs adaptive testing procedure.
result Achieves optimal rates and demonstrates phase transition phenomena in federated testing.
FMP improves personalized ASR models on private devices.
problem Limited accuracy and privacy in federated fine-tuning of ASR models.
method FMP estimates global and personalized marginal distributions and adjusts NNLM probabilities.
result FMP achieves modest WER reductions on ASR rescoring tasks.
Exponentially fast SMF algorithm for multi-class classification.
problem Learning interpretable features from high-dimensional data.
method Novel framework that 'lifts' SMF as a low-rank matrix estimation problem.
result Provable exponential convergence to global minimizer under mild assumptions.
New algorithms solve nonconvex federated learning problems efficiently.
problem Nonconvex federated composite optimization in federated learning.
method FedDR and asyncFedDR algorithms combining Douglas-Rachford splitting, randomized block-coordinate strategies, and asynchronous implementation.
result Match communication complexity lower bound up to a constant factor.
FedLCB-Q learns optimal policies from federated offline data with linear speedup.
problem Learning optimal policies from offline data with federated learning.
method Federated offline RL algorithm tailored for Q-learning, using local Q-function updates and central aggregation.
result Achieves linear speedup in sample complexity with collaboration among agents.
Paper addresses federated contextual bandits with encryption.
problem Building contextual bandits with vertically distributed data.
method Design of O3M encryption scheme for LinUCB and LinTS.
result Proposed protocols achieve good performance and privacy.
A new multi-view clustering method using deep matrix decomposition and partition alignment.
problem Improving multi-view clustering methods to better utilize data representations and view-specific structures.
method Deep matrix decomposition for partition representations, joint use of partition representations, and alternating optimization.
result Demonstrated effectiveness on six benchmark datasets compared to state-of-the-art methods.
This paper addresses privacy in federated learning with wireless clients and base stations.
problem Privacy of clients' data in federated learning with hierarchical wireless architecture.
method Derives communication cost limits and introduces private aggregation schemes tailored for hierarchical wireless systems.
result Private aggregation schemes reduce communication costs by multiplicative factors compared to information-theoretic limits.